**4. The steps in machine learning**

This study described in this chapter aims to predict whether a specific IF intervention would reduce the insulin resistance of an individual with prediabetes. The approach to answer this question is machine learning. The process of machine learning is composed of 5 major steps: The first is identifying the required data and gathering data from various sources. The next step is preparing and Pre-processing the data to have homogeneity. Then the model must be built by selecting the right Machine Learning classifier. The fourth step is to train and test the data and gain insights from the model results. Finally, we might want to improve results by feature selection for example.

**393**

**Table 1.**

*Selecting Intermittent Fasting Type to Improve Health in Type 2 Diabetes: A Machine Learning…*

In order to answer the question of this study, authors of 25 published papers that performed randomized clinical trials investigating the IF effects on T2D parameters were asked for the individual data. I received the individual data from 5 out 25 papers [42, 45–48]. The other authors replied that they could not submit the data

The selection criteria for this research were: basal fasting glucose above 5 mmol/L (90 mg/dL) or BMI (Body Mass Index) above or equal to 25. Those criteria were established since they indicate possible prediabetes [49]. The IDF's 2019 cutoff for fasting glucose indicating prediabetes is 100 mg/dL; we set the cutoff at 90 mg/dL. Finally, 254 individuals who answered the criteria were selected. **Table 1** contains the average values of the numerical attributes of the data. The average values show decrease in weight, BMI, fasting glucose and fasting insulin however we should remember that those are averages therefore we cannot conclude that all the interventions work all the time for all the people. This would be the query that the machine learning approach will investigate.

The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) has been proven to be a very sensitive test for indicating prediabetes [8]. Insulin resistance can be estimated from fasting glucose and insulin levels. This is shown in the

Prediabetes people or people with T2D usually have a significant insulin resistance. A high score of HOMA-IR indicates a significant insulin resistance. To learn the difference of HOMA-IR before and after the intervention the HOMA-IR using (Eq. (1)) was calculated twice for each of the 254 individuals. Once HOMA-IR was calculated for the basal values of fasting glucose and insulin and once for the values after the intervention.

This study contains 9 different types of interventions, starting from continuous energy restriction – through intermittent energy restriction for two days a week, or

44.3 87.5 81.1 32.1 31 4.99 4.93 61.1 53.6

**Basal After Basal After Basal After Basal After**

The difference between them represents the insulin resistance reduction.

**Age Weight BMI Fasting glucose** 

HOMA Fasting Glucose Fasting Insulin − = *IR* ∗ (1)

**(mmol/liter)**

**Fasting insulin (pmol/liter)**

*DOI: http://dx.doi.org/10.5772/intechopen.95336*

due to the confidentiality of the participants.

**4.1 Identifying required data**

**4.2 Processing the data**

*4.2.2 HOMA-IR equation*

HOMA-IR equation presented as follows:

*4.2.3 Types of intermittent fasting interventions*

*Average values of attribute in selected data.*

*4.2.1 Choosing people*

*Selecting Intermittent Fasting Type to Improve Health in Type 2 Diabetes: A Machine Learning… DOI: http://dx.doi.org/10.5772/intechopen.95336*
